1. Field of the Invention
This invention relates to the control system for an optical light source through use of a neural network. Although primarily intended for the fiber optics industry, applications extend to any industry that requires a stable optical source.
2. Background Information
There are numerous types of light sources currently in existence. However, the intensity and wavelength of the light that is produced may vary, depending on parameters such as voltage across the light source, current flowing through the light source, and temperature of the light source. Methods exist to minimize effects caused by variations in these parameters, in either an open loop or closed loop configuration. In the closed loop configuration, a feedback or error signal is provided to a control system that minimizes the error. In the open loop configuration, such feedback is not provided.
Conventional methods use a temperature sensor that is physically removed from the light source to determine the temperature of the light source for the purpose of monitoring and control. This suffers from the disadvantage that there will always be a slight variance in the temperature of the detector and the temperature of the light source. Furthermore, in a conventional system the temperature sensor and light source will almost always have different thermal time constants, indicating that even a system with perfect steady-state temperature compensation may not give the desired results in response to a change in temperature.
Laser diode sources typically use a monitor photodiode. The output current of the monitor photodiode is commonly considered to be proportional to the optical output power of the laser. However, such a practice generates errors since non-linearity may be introduced by temperature dependencies.
U.S. Pat. No. 6,411,046 teaches a model of LED parameters for use in white light control. The '046 patent uses a model of the optical power and wavelength output from an array of light emitting diodes. The model is dependent on derived polynomial equations and on the temperature measured by a temperature sensor in thermal contact with a heatsink to which the LED's are attached. The patent controls the current to the LED's in order to increase or decrease the optical power emitted by different colored LED's. One skilled in the art would appreciate that minor errors in the coefficients of the polynomial equation could adversely affect the performance of the device.
A very different method of determining output temperature can be found in U.S. Pat. No. 6,449,574. A resistance temperature device (RTD) is used to determine process control device diagnostics. An RTD is a device that changes resistance with temperature, allowing information to be extracted by passing a known current through the RTD and measuring the voltage across the RTD. However, parasitic voltages within the circuit cause voltage variations, the error of which the '574 patent attempts to reduce. Nevertheless, due to the voltage measurement error, this method is less effective and would not work well for precise control of the output wavelength of a light source.
U.S. Patent Application No. 2002/0149895 teaches a closed loop system to control the power supplied to a resistive load. The system contains a regulator circuit that sends power impulses to a pulse train generator circuit. The output of the generator circuit is a heating pulse train, which can be used to determine the temperature of the load through a calculation. This temperature-out value is sent to a temperature comparison circuit, which provides control to disconnect the power source from the load if the temperature-out value reaches a maximum temperature limit. The patent provides for only on/off operation of the device, rather than variable control. Furthermore, the method is a first order approximation of the temperature and more accurate estimates may be required to provide precise control of the optical output power of the light source.
Related to the '895 application, U.S. Pat. No. 6,349,023 also teaches a power control system for an array of lights. The system uses a model to determine the temperature of the load by sensing a voltage proportional to the power in a resistive load. If necessary, the power source is disconnected from the load if the high temperature limit is reached. Although the system operates in real-time, analog components are used.
Regarding the application of neural networks, typical applications are shown in U.S. Pat. Nos. 5,740,324 and 5,485,545. The '324 patent teaches a method of system identification of a process, based on a neural network and applied to a heating system. The patent explains that system identification problems are caused by the approximation of system parameters. Using neural networks can reduce these estimation errors. A three-layer feed forward neural network with a back propagation learning rule is used as the preferred embodiment for the neural network. The inputs to the neural network are the input and output of the process, and the outputs of the neural network are estimates of model parameters, requiring no mathematical analysis in between. The method has two stages—in the first stage a mathematical model is used to generate training data and is implemented as a computer program. Training data comprises examples of open loop responses of the system to a step input with different parameter values. The second phase consists of using the neural network in a teaching mode wherein one or more parameters are identified. In this stage it is assumed that every desired output is known for each training input.
The '545 patent uses a conventional controller in parallel with a neural network controller. The neural network goes through a learning step by forcing its input/output pairs to match that of the conventional controller. The patent further applies the teachings to a voltage/reactive-power controller to maintain levels suitable for high speed operation without the need to approximate the power characteristics of the system. Relearning also takes place to allow the neural network to update itself in accordance with a system simulator.
U.S. Pat. No. 5,111,531 also teaches a process control method though use of a neural network. The neural network, when trained, predicts the value of an indirectly controlled process variable and can be implemented through an integrated circuit or a computer program. Directly controlled process variables are changed accordingly to cause the predicted value to approach a desired value. The system consists of fast-acting controllable devices for changing controllable process variables, a computer for storing and executing rules related to operation of the neural network and a neural network. Examples of fast-acting devices are power supplies that control electrical heating currents or motors connected to valves. The computer contains the process description database that defines the state of the multi-variable process. As well, the computer must execute the rules associated with each input neuron to establish the value of the input neuron and execute the rules associated with the output neurons for establishing the set point values to be applied to the fast-acting devices. Rules generated for input neurons can comprise averaging, filtering, combining and/or switching rules, while output neuron rules may comprise limit checking, weighing, scaling and/or ratio rules. The neural network goes through a training process whereby several training sets of input neuron and output neuron values captured from the process while it is in operation are presented to the neural network and a back propagation algorithm adjusts the interconnection between neurons. Although useful, the '531 patent only provides an approach to controlling complex multi-variable continuous manufacturing processes.
Regardless of the type of light source, there generally exists a relationship between the applied voltage, the current, the temperature of the source, the optical power produced, and the wavelengths of light produced. This relationship may be quite complex or poorly understood, but it nonetheless exists. One object of the present invention is to use a neural network to provide a novel means for employing the relationship between the various input and output parameters without requiring a detailed or complete knowledge of the nature of the relationship.